Machine Learning: The Art and Science of Algorithms that Make Sense of Data: Peter Flach: 9781107422223: Amazon.com: Books

In real world, three cohorts would approach Machine Learning differently - A. Programmers - "How" - interested in quickly learning the libraries, tips/tricks to scale algorithms with larger data sets B. Theorists - "What" - interested in choosing the right algorithm, design ensemble, selecting and extracting right features C. Fashionists - "Show" - in this category, some of the even basic reporting/analytics are not termed "Machine Learning", need enough buzzwords pieced together to repaint the old apps. Flach's book is a great source for those who are 75%-25% between first two, and perhaps even greater especially if your Linear Algebra (basics) is not too rusty. It gives a wide and somewhat deep tour of the landscape broken into four paradigms (Quantitative/Analytical, Logical, Geometric, Probabilitisic) and does a real good job on feature design. The book is interspersed with some key insights that are not to be found elsewhere (e.g., how the'pseudo-inverse' in OLS is really decorrelate-scale-normalize the distribution; Skew-Kurtosis are the statistical measure of "shape"; Naive Bayes is not only Naive but also not particularly Bayesian; How Laplacian Estimate generalizes into Pseudo-Counts and then to m-estimate etc.). After "deep reading" of the book over a month or so, I also went through Flach's detailed 500 slide presentation (check out his website) on this book.